Urspeter Knecht

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Automatic estimation of extent of resection and residual tumor volume of patients with glioblastoma.

OBJECTIVE In the treatment of glioblastoma, residual tumor burden is the only prognostic factor that can be actively influenced by therapy. Therefore, an accurate, reproducible, and objective measurement of residual tumor burden is necessary. This study aimed to evaluate the use of a fully automatic segmentation method-brain tumor image analysis (BraTumIA)-for estimating the extent of resection (EOR) and residual tumor volume (RTV) of contrast-enhancing tumor after surgery. METHODS The imaging data of 19 patients who underwent primary resection of histologically confirmed supratentorial glioblastoma were retrospectively reviewed. Contrast-enhancing tumors apparent on structural preoperative and immediate postoperative MR imaging in this patient cohort were segmented by 4 different raters and the automatic segmentation BraTumIA software. The manual and automatic results were quantitatively compared. RESULTS First, the interrater variabilities in the estimates of EOR and RTV were assessed for all human raters. Interrater agreement in terms of the coefficient of concordance (W) was higher for RTV (W = 0.812; p < 0.001) than for EOR (W = 0.775; p < 0.001). Second, the volumetric estimates of BraTumIA for all 19 patients were compared with the estimates of the human raters, which showed that for both EOR (W = 0.713; p < 0.001) and RTV (W = 0.693; p < 0.001) the estimates of BraTumIA were generally located close to or between the estimates of the human raters. No statistically significant differences were detected between the manual and automatic estimates. BraTumIA showed a tendency to overestimate contrast-enhancing tumors, leading to moderate agreement with expert raters with respect to the literature-based, survival-relevant threshold values for EOR. CONCLUSIONS BraTumIA can generate volumetric estimates of EOR and RTV, in a fully automatic fashion, which are comparable to the estimates of human experts. However, automated analysis showed a tendency to overestimate the volume of a contrast-enhancing tumor, whereas manual analysis is prone to subjectivity, thereby causing considerable interrater variability.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time‐consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest‐based method for automatic quality assessment of 1H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non‐acceptable by two expert spectroscopists. To account for the effects of intra‐rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal‐to‐noise ratios (SNRs) in the ranges 50–75 ms and 75–100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUIs SpectrIm plugin. Copyright

CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

This paper extends a previously published brain tumor segmentation method with a dense Conditional Random Field (CRF). Dense CRFs can overcome the shrinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient cases for testing) and compared to a baseline method using a grid-structured CRF. Improved segmentation performance for the complete and enhancing tumor was observed with respect to grid-structured CRFs.

On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients.

To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI).

Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction

Uncertainty measures of medical image analysis technologies, such as deep learning, are expected to facilitate their clinical acceptance and synergies with human expertise. Therefore, we propose a full-resolution residual convolutional neural network (FRRN) for brain tumor segmentation and examine the principle of Monte Carlo (MC) Dropout for uncertainty quantification by focusing on the Dropout position and rate. We further feed the resulting brain tumor segmentation into a survival prediction model, which is built on age and a subset of 26 image-derived geometrical features such as volume, volume ratios, surface, surface irregularity and statistics of the enhancing tumor rim width. The results show comparable segmentation performance between MC Dropout models and a standard weight scaling Dropout model. A qualitative evaluation further suggests that informative uncertainty can be obtained by applying MC Dropout after each convolution layer. For survival prediction, results suggest only using few features besides age. In the BraTS17 challenge, our method achieved the 2nd place in the survival task and completed the segmentation task in the 3rd best-performing cluster of statistically different approaches.

P16.19THE EFFECTS OF BEVACIZUMAB ON MR-IMAGING AND 1H-MR-SPECTROSCOPY (MRS) IN PATIENTS WITH HIGH GRADE GLIOMAS.

INTRODUCTION: This prospective study is derived from a framework investigating effects of tumor progression on MRI/MRS-parameters in high grade gliomas (WHO III-IV) in patients undergoing different types of neuro-oncologic therapies. Preliminary data are presented here. We aim to identify MR-related parameters that segregate a. real tumor progression from pseudo tumor progression and b. real tumor response from pseudo-response, two effects which complicate appropriate tumor diagnostics. Forty-nine patients are currently enrolled into the study. The MRI/MRS findings of a subgroup of 9 patients who received a combined irradiation (39.9 Gy in 15 fractions) with the angiogenesis inhibitor Bevacizumab are discussed here. METHODS: All patients received multi parametric MRI encompassing DWI (ADC & DTI), Perfusion Imaging (PI), unenhanced and Gd-enhanced 3D-T1w, 3D-T2w, T2w-FLAIR, and MRS (Single Voxel Spectroscopy (SVS) and Chemical Shift Imaging (CSI), both at TE 30ms and TE135ms) before and after administration of Bevacizumab. The effects of Bevacizumab on PI, Gd-enhanced T1w and MRS was examined. For the evaluation of the MRS we used the Cho/Cr and the Cho/NAA peak-area ratio of qualitative MRS, for the Gd-enhanced T1w and PI we used a semi-quantitative classification. For statistical analysis T-Tests was performed. The study was approved by our local ethics committee. RESULTS: Pre-treatment with Bevacizumab, three and six month follow up MRIs revealed significant reductions in Gd-enhanced T1w MRI (p < 0.03). No significant differences were detected for MRS and PI (regional cerebral blood volume, rCBV). Statistical significant differences were also detected between preoperative and postoperative MRI for Gd-enhanced T1w and rCBV for all five time points during follow up (p < 0.011). However, MRS findings showed no improvement in all MRS parameters during follow up. DISCUSSION AND CONCLUSION: No significant improvements in spectroscopic parameters could be observed in response to Bevacizumab treatment. In contrast, the improvement in Gd-enhanced T1w MRI as well as MRI Perfusion does not reflect the true tumor response state in Bevacizumab treated patients. MRS findings seem to reflect tumor progression state better than MRI under Bevacizumab treatment. Our findings are in keeping with the current literature.